Back to partners
Partners
Image

Put Gemini to work in production

Agents grounded in your data. Evaluated against your standards.

Partnership spotlight

Snorkel's nearly 10-year strategic relationship with Google spans joint academic research, co-delivery with Fortune 100 clients, and a history of each organization building with the other's capabilities. Snorkel is a Google GenAI Trusted Tester, a founding Built with Google Cloud AI partner, and an inaugural member of the invite-only Google Cloud Springboard program. 

The Gemini model family, backed by Google Cloud's governance, compliance controls, and agentic infrastructure, is a preferred foundation for many global enterprises. Snorkel builds on Google AI to develop and deploy specialized agents grounded in enterprise-specific data, evaluated against real-world criteria, refined through the same evaluate-curate-refine loop used in frontier model development. When agents underperform, teams know exactly where, why, and what data to develop to fix it. 

The result is a production AI system that runs real business processes, earns end-user trust, and delivers measurable business impact.

Customer stories

View all stories
Telecommunications
Top 5 Global Telco
From stalled pilot to $43M annual ROI and 95% accuracy

$43M

Annual ROI

95%

Accuracy (up from 54%)

90%

of complex cases resolved in <24 hrs

Online Retail
Wayfair
Wayfair achieves 99% category win rate and 7-point clickthrough lift

5↑

point increase in add-to-cart rates

99%

win rate over previous SoTA models

10x

faster model development cycles

Federal
Federal Advisory Council on Historic Preservation
ACHP is building AI-native infrastructure for national-scale permitting
Image
AI-native geospatial system
Image
Spans federal agencies, all 50 states, territories, and tribal governments
Image
<1% hallucination: a wrong answer reroutes a pipeline or stalls a data center
Image
"The accuracy of any ML model is only as good as the data it was trained on. The combination of Snorkel AI's programmatic, data-centric approach with Google Cloud's ability to help organizations build, deploy and scale efficient AI models is a meaningful advantage."

Dr. Ali Arsanjani
Director of Cloud Partner Engineering

Image

Relevant research

View all research
Blog
Parsing Isn’t Neutral: Why Evaluation Choices Matter
Behind every AI benchmark is a hidden choice: how to read the model’s answers. That choice—parsing—can quietly tilt results more than the model itself. Parsing...
Blog
Introducing the Snorkel Agentic Coding Benchmark
Today, we’re sharing details about the Snorkel Agentic Coding benchmark—a comprehensive evaluation suite designed to test whether agents can handle the full complexity of software...
Benchmark
Open Benchmarks Grants
Continual Learning Bench
A benchmark evaluating whether AI systems genuinely improve from prior experience. Unlike static benchmarks that treat every task as independent, Continual Learning Bench measures performance...
Research Paper
Accepted to MLSys 2026
Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes
Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground truth answers in the case of...
Share this article

For AI agents that need to be right, not just good enough

Talk to a strategist